An Algorithm for Detection of Prohibited Items in X-ray Images Based on Improved YOLOv4

doi:10.3969/j.issn.1000-1093.2021.12.015

Abstract

Abstract: An improved YOLOv4 algorithm for detecting the prohibited items in X-ray images is proposed to increase the speed of security inspection and realize the automatic detection of prohibited items in X-ray images. The proposed algorithm is used to design a dilated dense convolution module based on the one-stage object detection algorithm YOLOv4. The features after the upsampling link fusion are input into the dilated dense convolution module to enhance the feature expression ability and the convolution field of vision. An attention mechanism is added to the fused feature information to enhance effective features and suppress invalid features. Finally，a feature map representing image information is input to detection head. Mosaic data enhancement method is used to train the network to improve the robustness of the network. The results show that the mean average precision (mAP) of the proposed algorithm on the public SIXray data set reaches 80.16%，and the detection speed is 25 frames per second (FPS). The proposed algorithm can achieve high detection accuracy for multiple types of prohibited items on the public SIXray dataset， and meet the real-time requirements of detection.

Key words: prohibiteditemsdetection, YOLOv4, X-rayimage, dilateddenseconvolution, attentionmechanism, dataaugmentation

CLC Number:

TP391.4

MU Siqi， LIN Jinjian， WANG Haiquan， WEI Xiongzhi. An Algorithm for Detection of Prohibited Items in X-ray Images Based on Improved YOLOv4[J]. Acta Armamentarii, 2021, 42(12): 2675-2683.

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